The present invention relates an access control mechanism, for a telecommunications device, for example an ATM switch.
It is widely expected that ATM networks will enable the satisfaction of performance requirements of a whole variety of services, such as voice, video, and data on a single infrastructure. To achieve this, ATM networks must provide traffic control management.
A problem which arises in ATM switches is due to the random nature of ATM traffic. The switch control elements must be able to cope with variations in traffic.
The performance of an ATM network can be defined in terms of the Quality of Services (QoS) requirements which must be delivered to the network subscriber. These performance measures include: the network throughput, latency, jitter or delay variation, and the amount of cell loss that can be tolerated.
A network provider may agree to handle a certain number of cells (units of traffic) from a particular subscriber. It is then necessary for the network provider to have an access control system, which verifies that the subscriber is not supplying excess traffic to the network. In the limit, this excess traffic might not be allowed to access the network, depending on demands from other users.
The present invention provides a method for open-loop adaptive control of access to a broadband packet data type telecommunications network. Based on expected future data arrival patterns, data which violate agreed quality of service parameters may be denied access to the network.
The present invention relates to the use of neural networks, specifically pRAM (probabilistic RAM), networks in ATM switch design, as part of the access control system. Since the pRAM learns very fast, and has been shown to posses excellent generalisation properties in noisy environments, it is very suitable to cope with the stochastic nature of broadband ATM traffic. A pyramidal probabilistic RAM, configured in a teacher forcing mode, provides the required control. The pRAM may be trained off-line, before being integrated as the control element of an ATM node, or on-line.
Specifically, the neural network is trained to be able to predict incoming traffic flows to the switch. If incoming data is such that it is probable, based on the expected future traffic, that such future traffic will be unable to access the switch, then the incoming data may be denied access to the switch.